A basic system that tells about the stage/level (class) of cancer a patient has that will help the doctors to provide a personalised medicine to the patients. The data used is taken from Kaggle Competition and trained using Machine Learning algorithms to gain the best accuracy.
This project aims to stablishing a diagnosis through exome sequencing can provide potential benefits to patients, insurance companies, and the healthcare system. Genetic data is being produced in vast amounts which needs careful organization. Hence, diagnostic sequencing is being employed increasingly. I have implemented various machine learning methods which accurately assess the clinical validity of gene-disease relationships to interpret new research findings in a clinical context which in turn increases the diagnostic rate. The solution to deal with huge data effectively is by using Machine learning. Medical science yields huge amount of data on daily basis from research and development (R&D), physicians and clinics, patients, care givers etc. Diagnosis via machine learning works when the condition can be reduced to a classification task on physiological data, in areas where we currently rely on the clinician to be able to visually identify patterns that indicate the presence or type of the condition. A lot has been said during the past several years about how precision medicine and, more concretely, how genetic testing is going to disrupt the way diseases like cancer are treated. But this is only partially happening due to the huge amount of manual work still required. This project aims at building a machine learning model to take personalized medicine to its full potential.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent